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Spatiotemporal analysis for fighting COVID-19 in Iraq

    Maythm Al-Bakri   Affiliation

Abstract

At the end of 2019, a new form of Coronavirus (later dubbed COVID-19) emerged in China and quickly spread to other regions of the globe. Despite the virus’s unique and unknown characteristics, it is a widely distributed infectious illness. Finding the geographical distribution of the virus transmission is therefore critical for epidemiologists and governments in order to respond to the illness epidemic rapidly and effectively. Understanding the dynamics of COVID-19’s spatial distribution can help to understand the pandemic’s scope and effects, as well as decision-making, planning, and community action aimed at preventing transmission. The main focus of this study is to investigate the geographic patterns of COVID-19 dissemination in Iraq from May 1 to July 29, 2021. The analysis was primarily based on using spatial analysis tools such as standard deviational ellipse (SDE) with in GIS environment, in addition to incidence rates calculations. The results revealed that the direction of COVID-19 spread is NW-SE. Furthermore, the findings showed that the rate of COVID-19 infections is greater at the middle and south of Iraq. This may aid decision-makers in identifying priority areas for emergency efforts.

Keyword : COVID-19, spatial distribution, GIS, spatial analysis, standard deviational ellipse, Iraq

How to Cite
Al-Bakri, M. (2022). Spatiotemporal analysis for fighting COVID-19 in Iraq. Geodesy and Cartography, 48(4), 233–242. https://doi.org/10.3846/gac.2022.15682
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Dec 12, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Al-Kindi, K. M., Alkharusi, A., Alshukaili, D., Al Nasiri, N., Al-Awadhi, T., Charabi, Y., & El Kenawy, A. M. (2020). Spatiotemporal assessment of COVID-19 spread over Oman using GIS techniques. Earth Systems and Environment, 4, 797–811. https://doi.org/10.1007/s41748-020-00194-2

Arab-Mazar, Z., Sah, R., Rabaan, A. A., Dhama, K., & Rodriguez-Morales, A. J. (2020). Mapping the incidence of the COVID-19 hotspot in Iran – Implications for travellers. Travel Medicine and Infectious Disease, 34, 101630. https://doi.org/10.1016/j.tmaid.2020.101630

Bashir, M. F., Ma, B., Bilal, Komal, B., Bashir, M. A., Tan, D., & Bashir, M. (2020). Correlation between climate indicators and COVID-19 pandemic in New York, USA. The Science of the Total Environment, 728, 138835. https://doi.org/10.1016/j.scitotenv.2020.138835

Central Statistical Organization Iraq. (2021). Retrieved April, 21, 2021, from http://cosit.gov.iq/ar/62arabic-cat/indicators/174-population-2?jsn_setmobile=no

Cicalò, E., & Valentino, M. (2019). Mapping and visualisation on of health data. The contribution on of the graphic sciences to medical research from New York yellow fever to China coronavirus. Disegnarecon, 12(23), 12–21.

Hazbavi, Z., Mostfazadeh, R., Alaei, N., & Azizi, E. (2021). Spatial and temporal analysis of the COVID-19 incidence pattern in Iran. Environmental Science and Pollution Research, 28, 13605–13615. https://doi.org/10.1007/s11356-020-11499-0

Kamel Boulos, M. N., & Geraghty, E. M. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syn-drome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the glob-al fight against outbreaks and epidemics. International Journal of Health Geographics, 19, 8. https://doi.org/10.1186/s12942-020-00202-8

Kang, D., Choi, H., Kim, J.-H., & Choi, J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases, 94, 96–102. https://doi.org/10.1016/j.ijid.2020.03.076

Lima, E. E. C., Gayawan, E., Baptista, E. A., & Queiroz, B. L. (2021). Spatial pattern of COVID-19 deaths and infections in small areas of Brazil. PLoS ONE, 16(2), e0246808. https://doi.org/10.1371/journal.pone.0246808

Martellucci, C. A., Sah, R., Rabaan, A., Dhama, K., Casalone, C., Arteaga-Livias, K., Sawano, T., Ozaki, A., Bhandari, D., Higuchi, A., Kotera, Y., Fathah, Z., Roy, N., Ateeq, M., Rahman, U., Tanimoto, T., & Rodriguez-Morales, A. (2020). Changes in the spatial distribution of COVID-19 inci-dence in Italy using gis-based maps. Annals of Clinical Microbiology and Antimicrobials, 19, 30. https://doi.org/10.1186/s12941-020-00373-z

Murugesan, B., Karuppannan, S., Mengistie, A. T., Ranganathan, M., & Gopalakrishnan, G. (2020). Distribution and trend analysis of COVID-19 in India: Geospatial approach. Journal of Geographical Studies, 4(1–2), 1–9. https://doi.org/10.21523/gcj5.20040101

OCHA. (2020). WHO: With COVID-19 cases in Iraq at an alarming level, effective ways to prevent community-wide transmission is to avoid mass gatherings, exercise social distancing and wear masks in public [EN/AR/KU]. https://reliefweb.int/report/iraq/who-covid-19-cases-iraq-alarming-level-effective-ways-prevent-community-wide

OHCHR. (2021). The Iraqi Government’s measures to confront the corona virus (in Arabic). Retrieved August 22, 2021, from https://www.ohchr.org/Documents/Issues/Development/seminar-contribution-development/1st-study/states/Iraq-25-02-2021.docx

Oyana, T. J., & Margai, F. M. (2016). Spatial analysis: Statistics, visualization, and computational methods. Taylor and Francis Group, LLC. https://doi.org/10.1201/b18808

Peng, J., Chen, S., Lü, H., Liu ,Y., & Wu, J. (2016). Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011. Remote Sensing of Environment, 174, 109–121. https://doi.org/10.1016/j.rse.2015.12.008

Rezaei, M., Nouri, A. A., Park, G. S., & Kim, D. H. (2020). Application of geographic information system in monitoring and detecting the COVID-19 outbreak. Iranian Journal of Public Health, 49(S1), 114–116. https://doi.org/10.18502/ijph.v49iS1.3679

Sarhan, A. R., Flaih, M. H., Hussein, T. A., & Hussein, K. R. (2020). Novel coronavirus (COVID-19) outbreak in Iraq: The first wave and future scenario. medRxiv 2020. https://doi.org/10.1101/2020.06.23.20138370

Sarwar, S., Waheed, R., Sarwar, S., & Khan, A. (2020). COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions?, The Science of The Total Environment, 730, 139089. https://doi.org/10.1016/j.scitotenv.2020.139089

Silalahi, F. E. S., Hidayat, F., Dewi, R. S., Purwono, N., & Oktaviani, N. (2020). GIS-based approaches on the accessibility of referral hospital using network analysis and the spatial distribution model of the spreading case of COVID-19 in Jakarta, Indonesia. BMC Health Service Research, 20, 1053. https://doi.org/10.1186/s12913-020-05896-x

University of South Florida. (2021). COVID-19 incidence rate. Retrieved August 20, 2021, from https://www.usf.edu/business/state-of-the-region/e-insights-2021/section-2-01-covid-19-incidence-rate.aspx

Wang, B., Shi, W., & Miao, Z. (2015). Confidence analysis of standard deviational ellipse and its extension into higher dimensional Euclidean space. PLoS ONE, 10(3), e0118537. https://doi.org/10.1371/journal.pone.0118537

World Bank. (2017). Iraq – systematic country diagnostic. Washington, D.C.

Worldometer. (2021). Coronavirus cases of Iraq. Retrieved August 20, 2021, from https://www.worldometers.info/coronavirus/country/iraq/